R Notebook
- Get the proportions of main cell types from each FACS sorted sample
library(Seurat)
library(dplyr)
library(Matrix)
Attaching package: ‘Matrix’
The following object is masked from ‘package:reshape’:
expandlibrary(ggplot2)
Read in the 4 sorted Populations separately processed and annotated
Check each object for main cell type labels
table(Neurons1$Cell_Types)
dim(Neurons1) # total cells 1723
table(Neurons2$Cell_Types)
dim(Neurons2) # total cells 8884
table(Astro$Cell_Type)
dim(Astro) # 20000
table(RG$Cell_Types)
dim(RG)
Fix each separate populations - Add FACS sort name - Down sample - check proportions are maintained - rename Cell_Type in Astro to be the same name for ‘Cell_Types’
# cell type variables weren't named the same and the project ID are not the population names
# I'll further down sample Glia1 (LaunchSample3)
# change orig.ident names
Idents(Neurons2) <- 'orig.ident'
Neurons2$orig.ident <- 'Neurons2'
Idents(Astro) <- 'orig.ident'
Astro$orig.ident <- 'Astrocytes'
Idents(RG) <- 'orig.ident'
RG$orig.ident <- 'RadialGlia'
# rename metadata
Idents(Astro) <- 'Cell_Type'
Astro$Cell_Types <- Idents(Astro)
table(Astro$Cell_Types)
Astro$Cell_Type <- NULL
# down sample
Idents(Astro) <- 'orig.ident'
Astro.sub <- subset(Astro, downsample = 9000)
table(Astro.sub$Cell_Types)
# good proportions are maintained
# save downsampled object
all.pops <- merge(Neurons1, y = c(Neurons2, Astro.sub, RG), project = "PhenoID")
Try to down sample to get closer proportions of starting populations
# down sample
Idents(Astro) <- 'orig.ident'
Astro.sub <- subset(Astro, downsample = 3000)
table(Astro$Cell_Types)
table(Astro.sub$Cell_Types)
# down sample
Idents(Neurons2) <- 'orig.ident'
Neurons2.sub <- subset(Neurons2, downsample = 2000)
dim(Neurons2)
table(Neurons2$Cell_Types)
table(Neurons2.sub$Cell_Types)
# down sample
Idents(RG) <- 'orig.ident'
RG.sub <- subset(RG, downsample = 2000)
dim(RG)
table(RG$Cell_Types)
table(RG.sub$Cell_Types)
all.pops <- merge(Neurons1, y = c(Neurons2.sub, Astro.sub, RG.sub), project = "PhenoID")
table(all.pops$orig.ident)
Check merge
table(all.pops$orig.ident)
table(all.pops$Cell_Types)
# I might want to down sample all the other populations accept Neurons1
# they cluster fairly well
# save the merged object
pathway <- "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/"
saveRDS(all.pops, paste(pathway,"All4popssub07102022.RDS"))
#seu.q <- readRDS(paste(pathway,"All4pops07102022.RDS"))
#seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/All4popssub07102022.RDS")
Normalize and cluster
# standard workflow
# repeated same for all.pops and all.pops sub
seu <- NormalizeData(all.pops, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2500)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
ElbowPlot(seu, ndims = 30)
# best choice PC = 15
# didn't save the objects need to repeat
seu.q <- NormalizeData(seu.q, normalization.method = "LogNormalize", scale.factor = 10000)
seu.q <- FindVariableFeatures(seu.q, selection.method = "vst", nfeatures = 2500)
seu.q <- ScaleData(seu.q)
seu.q <- RunPCA(seu.q)
ElbowPlot(seu.q, ndims = 30)
Find clusters
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 25, dims = 1:20)
DimPlot(seu, reduction = "umap")
seu.q <- FindNeighbors(seu, dims = 1:20, k.param = 25)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.6,1,1.2, 1.5, 1.8))
# clear out other clustering to be able to use clustree
library(clustree)
#columns.to.remove <- c("RNA_snn_res.0.05", "RNA_snn_res.0.1","RNA_snn_res.0.25","RNA_snn_res.0.5",
# "RNA_snn_res.0.4","RNA_snn_res.0.8")
#for(i in columns.to.remove) {
# seu.q[[i]] <- NULL
#}
#DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.05')
#DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.1')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2', label = TRUE)
#DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6',label = TRUE)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1', label = TRUE)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.8', label = TRUE)
DimPlot(seu.q, group.by = 'orig.ident', label = TRUE)
#VlnPlot(seu.q, features = 'nFeature_RNA', group.by = 'orig.ident')
DimPlot(seu.q, group.by = 'Cell_Types', label = TRUE)
DimPlot(seu.q, group.by = 'Cell_Types', split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'RNA_snn_res.1.8', split.by = 'orig.ident')
# repeat when object wasn't saved
seu.q <- RunUMAP(seu.q, reduction = "pca", n.neighbors = 25, dims = 1:20)
DimPlot(seu.q, reduction = "umap")
seu.q <- FindNeighbors(seu.q, dims = 1:20, k.param = 25)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.6,1,1.2, 1.8))
# clear out other clustering to be able to use clustree
library(clustree)
columns.to.remove <- c("RNA_snn_res.0.05", "RNA_snn_res.0.1","RNA_snn_res.0.25","RNA_snn_res.0.5",
"RNA_snn_res.0.4","RNA_snn_res.0.8")
for(i in columns.to.remove) {
seu.q[[i]] <- NULL
}
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.8', label = TRUE)
DimPlot(seu.q, group.by = 'orig.ident', label = TRUE)
DimPlot(seu.q, group.by = 'Cell_Types', label = TRUE)
DimPlot(seu.q, group.by = 'Cell_Types', split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'RNA_snn_res.1.8', split.by = 'orig.ident')
clustree(seu.q)
Number FACS populations in each cluster Number of Cell Types in each cluster
# FACS populations per cluster
table(seu.q$RNA_snn_res.1.8, seu.q$orig.ident)
# Cell Types per cluster
table(seu.q$RNA_snn_res.1.8, seu.q$Cell_Types)
Re annotate these clusters with subgroups
Idents(seu.q) <- 'RNA_snn_res.1.8'
# from k.param 49
cluster.ids <- c("Astro1","Neurons1","RG1","Astro2",
"RG2","Neurons2","Neurons3","Astro3",
"Neurons3","Astro4","Astro5","Neurons4",
"RG3","RG4","Neurons5","Neurons6",
"Endothelial","Neurons7")
# from k.param 249
cluster.ids <- c("RG1","Neurons1","Astro1","Astro2","Neurons2",
"Astro-Mix","Neurons-RG","RG2",
"Neurons3",
"Endothelial")
# k.param 125
cluster.ids <- c("Astro1","Neurons1","Astro2","Neurons1",
"RG1-Neurons","Neurons2","Astro3-Neurons-RG","RG1",
"Neurons3","Neurons4","RG2",
"Endothelial","Mix")
# k.param 25
cluster.ids <- c("Astro1","Neurons1","RG-Neurons1","Astro2","Neurons2",
"Astro-RG","Astro3","Neurons2","RG2","Neurons3",
"Astro3","Neurons4","RG3","Neurons4","Neurons5",
"Mix","RG4","Endothelial","Neurons6","RG-Neurons2",
"Neurons7","Glia","RG5","Mix","Mix"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cluster.ids <- Idents(seu.q)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cluster.ids', repel = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cluster.ids',split.by = 'orig.ident')
# some cluster are problematic
# cluster 3 has 650 RG and 180 Neurons
Idents(seu.q) <- 'RNA_snn_res.1.8'
cluster.ids <- c("Astro","Neurons","RG","Astro",
"RG","Neurons","Neurons","Astro",
"Neurons","Astro","Astro","Neurons",
"RG","RG","Neurons","Neurons",
"Endothelial","Neurons")
cluster.ids <- c("RG","Neurons","Astro","Astro","Neurons",
"Astro-Mix","Neurons-RG","RG",
"Neurons",
"Endothelial")
# k.param 125
cluster.ids <- c("Astro","Neurons","Astro","Neurons",
"RG1-Neurons","Neurons","Astro3-Neurons-RG","RG",
"Neurons","Neurons","RG",
"Endothelial","Mix")
# K 25
cluster.ids <- c("Astro","Neurons","RG-Neur","Astro","Neurons",
"Astro-RG","Astro","Neurons","RG","Neurons",
"Astro","Neurons","RG","Neurons","Neurons",
"Mix","RG","Endothelial","Neurons","RG-Neur",
"Neurons","Glia","RG","Mix","Mix"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Level1 <- Idents(seu.q)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Level1', repel = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Level1',split.by = 'orig.ident')
Make a table of the proportion of cell types and a plot
cell.order
Error: object 'cell.order' not foundMake a proprotion table
library(stringr)
library(reshape)
# for original cell types
df.r <- reshape(df, idvar = "Var2", timevar = "Var1", direction = "wide")
dat1 <- df.r
dat1[] <- lapply(dat1[], function(x){
# Check if the column is numeric
if (is.numeric(x)){
return(x/sum(x)*100)
} else{
return(x)
}
})
dat1
write.csv(dat1, paste(output_path,"proportionofCelltypesin4pops.csv"))
### for new main cell types
df.r <- reshape(df.3, idvar = "Var2", timevar = "Var1", direction = "wide")
dat3 <- df.r
dat3[] <- lapply(dat3[], function(x){
# Check if the column is numeric
if (is.numeric(x)){
return(x/sum(x)*100)
} else{
return(x)
}
})
dat3
### with the subtypes of cells
df.r <- reshape(df.2, idvar = "Var2", timevar = "Var1", direction = "wide")
dat2 <- df.r
dat2[] <- lapply(dat2[], function(x){
# Check if the column is numeric
if (is.numeric(x)){
return(x/sum(x)*100)
} else{
return(x)
}
})
dat2
Process Kamath
seu <- NormalizeData(seu.r, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
saveRDS(seu,"/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/KamathTotal_downsample.RDS")
Re annotate the new clustered object
Get predictions: Use Kamath all cells down sampled (doesn’t contain Radial Glia) Developing Forebrain Organoids AIW002 120 days
# Kamath midbrain Postmortem sn
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/KamathTotal_downsample.RDS")
#Idents(seu.r) <- "Cell_Type"
#seu.r <- subset(seu.r, idents = c("astro","da","nonda","endo"))
Idents(seu.r) <- "Cell_Subtype"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Subtype, k.weight = 50)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "mb.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$mb.pred)
seu.q$mb.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.7, seu.q$mb.pred, "none")
DimPlot(seu.q, group.by = 'mb.pred.thresh')
table(seu.q$mb.pred.thresh)
#***** subtype predictions don't work
#but when I had the separate subytes they did work on neurons and astro separately. It could be there just aren't enough reference cells - I only have 1000 each main group.
# I'm going to try with the main cell types groups instead
Idents(seu.r) <- "Cell_Type"
table(seu.r$Cell_Type)
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Type)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "mb.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$mb.pred)
seu.q$mb.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.7, seu.q$mb.pred, "none")
DimPlot(seu.q, group.by = 'mb.pred.thresh', label = TRUE)
table(seu.q$mb.pred.thresh)
# create the prediction top 2 cell types per cluster dataframe
# still useless
# best to use the subtype predictions from the separate objects after subsetting
Try the Bhaduri whole brain dataset
# midbrain and striatum
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_midbrain_striatum.RDS")
Idents(seu.r) <- "cell_cluster"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cell_cluster)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "mbs.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$mbs.pred)
seu.q$mbs.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.7, seu.q$mbs.pred, "none")
DimPlot(seu.q, group.by = 'mbs.pred.thresh', label = TRUE)
table(seu.q$mb.pred.thresh)
# read in the reference dataset
# whole brain Bhaduri down sampled
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_downsample.RDS")
Idents(seu.r) <- "cell_cluster"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cell_cluster)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "brain.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$brain.pred)
seu.q$brain.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.7, seu.q$brain.pred, "none")
DimPlot(seu.q, group.by = 'brain.pred.thresh', label = TRUE)
DimPlot(seu.q, group.by = "brain.pred")
table(seu.q$brain.pred.thresh)
Label transfer with the developing forbrain
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Karolinski_DevForebrain_downsample_Level1.RDS")
colnames(seu.r@meta.data)
DefaultAssay(seu.r) <- 'RNA'
# clusters has subgroups
# levels are main cell groups
Idents(seu.r) <- "Level1"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Level1)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "dfb.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$dfb.pred)
seu.q$dfb.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.85, seu.q$dfb.pred, "none")
table(seu.q$dfb.pred.thresh)
DimPlot(seu.q, group.by = 'dfb.pred.thresh', label = FALSE)
DimPlot(seu.q, group.by = "dfb.pred")
DimPlot(seu.q, group.by = 'orig.ident', split.by = 'dfb.pred.thresh')
DimPlot(seu.q, split.by = 'orig.ident', group.by = 'dfb.pred.thresh')
AIW002 120 days predictions
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")
### maybe the knockouts are causing a problem
Idents(seu.r) <- 'orig.ident'
seu.r <- subset(seu.r, idents = )
Idents(seu.r) <- "res08names"
DefaultAssay(seu.r) <- "RNA"
anchors <- FindTransferAnchors(reference = seu.r ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$res08names)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "hmo.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$hmo.pred)
seu.q$hmo.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.85, seu.q$hmo.pred, "none")
table(seu.q$hmo.pred.thresh)
DimPlot(seu.q, group.by = 'hmo.pred.thresh', label = FALSE)
DimPlot(seu.q, group.by = "hmo.pred")
DimPlot(seu.q, group.by = 'orig.ident', split.by = 'hmo.pred.thresh')
DimPlot(seu.q, split.by = 'orig.ident', group.by = 'hmo.pred.thresh')
Make a summary table of predictions
# AIW002 120 days predictions - take the thresholded options
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.1.8, seu.q$hmo.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
Try the 60 days organoids AIW
seu.r <-readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")
Idents(seu.r) <- "cluster.ids"
DefaultAssay(seu.r) <- "RNA"
anchors <- FindTransferAnchors(reference = seu.r ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cluster.ids)
seu.q <- AddMetaData(seu.q, predictions$predicted.id, col.name = "hmo60.pred")
seu.q <- AddMetaData(seu.q, predictions$prediction.score.max, col.name = "prediction.score.max")
table(seu.q$hmo60.pred)
seu.q$hmo60.pred.thresh <- ifelse(seu.q$prediction.score.max > 0.85, seu.q$hmo60.pred, "none")
table(seu.q$hmo60.pred.thresh)
DimPlot(seu.q, group.by = 'hmo60.pred.thresh', label = FALSE)
DimPlot(seu.q, group.by = "hmo60.pred")
DimPlot(seu.q, group.by = 'orig.ident', split.by = 'hmo60.pred.thresh')
DimPlot(seu.q, split.by = 'orig.ident', group.by = 'hmo60.pred.thresh')
Make predictions from 60 days
# AIW002 120 days predictions - take the thresholded options
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.1.8, seu.q$hmo60.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
Look at the expression of gene lists
Idents(seu.q) <- 'RNA_snn_res.1.8'
feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")
#DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list) +RotatedAxis()
PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
#DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()
ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")
feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
#DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list)+RotatedAxis()
# no proliferation marker expression PCNA or MKI67
# cluster 4 DA neurons - shows early neuron marker and low PAX 4
# cluster 3 has higher SOX2 - neuroblast marker / NPC marker
mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
#DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
#DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
#DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression
### glia markers
microglia = c("PTPRC","AIF1","ADGRE1") # ADGRE1 is a microglia marker F4/80, CD45 is PTPRC, gene name IBA1 is AIF1
astolgNPCpromicro = c("GFAP","S100B","SLC1A2","MBP","SOX10","SPP1","DCX","NEUROD1","TBR1","PCNA","MKI67","PTPRC","AIF1","ADGRE1")
# note GLT1 is EAAT2 which is SLC1A2 glutatmate transporter
# epithelial
epi = c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1") # e-cadherin is CDH1
#DoHeatmap(seu.q, features = astolgNPCpromicro, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = astolgNPCpromicro)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression
#DoHeatmap(seu.q, features = epi, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = epi)+RotatedAxis()
# also add Radial glia marker overlap with Glia and Neurons
features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
"ITGB5","IL6ST")
#DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = features)+RotatedAxis()Rename cell types
Idents(seu.q) <- 'RNA_snn_res.1.8'
cluster.ids <- c("Astro-RG-Pre","Neurons1-im","RG1-NPC","Astro1","Neurons2-im",
"Astro-NPC","Astro","Neurons3","RG2","Neurons4",
"Astro3","Neurons5","RG3-NPC","Neurons6-DA","Neurons7-NPC",
"Mix-RG4","RG5","Endothelial-NPC","Neurons8-inh-max","Neurons9-DA",
"Neurons10-DA","RG6","RG7-epi","NPC-Astro","RG-OPC"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Level2 <- Idents(seu.q)
DimPlot(seu.q, group.by = 'Level2', label = TRUE)
DimPlot(seu.q, group.by = 'Level2', label = TRUE, split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'orig.ident')
# name the major groups together and see how it matches up with space
cluster.ids <- c("Astro1","Neurons1","RG1","Astro2","Neurons2",
"Astro3","Astro4","Neurons3","RG2","Neurons4",
"Astro5","Neurons5","RG3-NPC","DANeurons1","Neurons6",
"Mix","RG4","Endothelial-NPC","Neurons8-inh-max","DANeurons2",
"DANeurons3","RG5","RG6-epi","RG7","RG-OPC"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Level3 <- Idents(seu.q)
DimPlot(seu.q, group.by = 'Level3', label = TRUE, repel = TRUE)
DimPlot(seu.q, group.by = 'Level3', label = TRUE, split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'orig.ident')
Idents(seu.q) <- 'RNA_snn_res.1.8'
cluster.ids <- c("Astro","Neurons","RG","Astro","Neurons",
"Astro","Astro","Neurons","RG","Neurons",
"Astro","Neurons","RG","DANeurons","Neurons",
"Mix","RG","Endothelial","Neurons","DANeurons",
"DANeurons","RG","RG","RG","RG"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Levels <- Idents(seu.q)
DimPlot(seu.q, group.by = 'Levels', label = TRUE, repel = TRUE)
DimPlot(seu.q, group.by = 'Levels', label = TRUE, split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'orig.ident')
See how the labels fit with sorted cell types
t.lables <- as.data.frame(table(seu.q$orig.ident, seu.q$Level3))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
#Not a bad breakdown mix
t.lables <- as.data.frame(table(seu.q$orig.ident, seu.q$Levels))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
Make the cell type proportion plots - make the input colours should be different than cell type colours Cell type Use colours from other figures if possible: Astrocytes = orange Orange Radial Glia = Pink Neurons = Purple Endothelial bright blue
I’ll make a UMAP with the input colours - match to 2D -
Find Cluster markers with all clusters:
Idents(seu.q) <- 'Level3'
ClusterMarkers <- FindAllMarkers(seu.q)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(seu.q, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
write.csv(output_path,"ClusterMarkersLevel3all.csv")
Same cell type markers are overlapping
# error in dot plot because of repeat gene
marker.top3 <- unique(top3$gene)
DotPlot(seu.q, features = marker.top3) + RotatedAxis()
# RG-NPC is similar to mix and DAneurons2
Not all astros overlap with astro markers and not all neurons overlap neurons The RG overlap with all kinds of cells.
Subset out populations for some label transfers from the public data.
Subset out Neurons
# subset neurons
# Level2
cluster.ids <- c("Astro-RG-Pre","Neurons1-im","RG1-NPC","Astro1","Neurons2-im",
"Astro-NPC","Astro","Neurons3","RG2","Neurons4",
"Astro3","Neurons5","RG3-NPC","Neurons6-DA","Neurons7-NPC",
"Mix-RG4","RG5","Endothelial-NPC","Neurons8-inh-max","Neurons9-DA",
"Neurons10-DA","RG6","RG7-epi","NPC-Astro","RG-OPC"
)
# Level 3
cluster.ids <- c("Astro1","Neurons1","RG1","Astro2","Neurons2",
"Astro3","Astro4","Neurons3","RG2","Neurons4",
"Astro5","Neurons5","RG3-NPC","DANeurons1","Neurons6",
"Mix","RG4","Endothelial-NPC","Neurons8-inh-max","DANeurons2",
"DANeurons3","RG5","RG6-epi","RG7","RG-OPC"
)
Idents(seu.q) <- 'Level3'
neuron.id <- c("Neurons1","Neurons2","Neurons3","Neurons4",
"Neurons5","DANeurons1","Neurons6",
"Mix","Neurons8-inh-max","DANeurons2",
"DANeurons3")
# without mix
neuron.id <- c("Neurons1","Neurons2","Neurons3","Neurons4",
"Neurons5","DANeurons1","Neurons6",
"Neurons8-inh-max","DANeurons2",
"DANeurons3")
neurons.sub <- subset(seu.q, idents = neuron.id)
table(neurons.sub$Level3)
DimPlot(neurons.sub, label = TRUE)
Find Cluster markers to distinguish Neuron subgroups
table(neurons.sub$Level3)
Idents(neurons.sub) <- 'Level3'
ClusterMarkers <- FindAllMarkers(neurons.sub, only.pos = TRUE)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(neurons.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersLevel3NeuronSubnoMix.csv", sep = ""))
Neurons 1 has overlapping markers with DAneurons2 Neurons 2, Neurons 4, Neurons 6 are very similar in the top markers - these are all in the Neurons1 FACS pop And the top markers are mito genes.
Look at the DA marker genes and see if there are differences there Also some neuronal markers
DA neurons 1 has the most different DA markers (TH, CALB1, SLC17A6, OTX2) - highest CALB1 DA neurons 2 has most cells and highest level of TH expression, most cells expressing SOX2 at a low levels DA neurons 3 SLC17A6, OTX2, OTX2 highest
Get markers for just DA neurons
Idents(neurons.sub) <- 'Level3'
DA.sub <- subset(neurons.sub, idents = c("DANeurons1","DANeurons2","DANeurons3"))
ClusterMarkers.DA <- FindAllMarkers(DA.sub, only.pos = TRUE)
top3 <- ClusterMarkers.DA %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(DA.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons.sub, features = marker.top3) + RotatedAxis()
DotPlot(DA.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersDANeurons.csv", sep = ""))
Good markers DA1 - RAB3B DA2 - TPBG or HES1 - TPBG is proposed as a DA marker (HES1 midbrain patterning) DA3 - TPH1 - sertononergic neurons or TTR oxidative stress protective
Compare the other neurons and find markers
DoHeatmap(neur.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons.sub, features = marker.top3) + RotatedAxis()
DotPlot(neur.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers.neur, paste(output_path,"ClusterMarkersotherNeurons.csv", sep = ""))
Have a look at some markers in feature plots
DimPlot(neur.sub)
FeaturePlot(neur.sub, features = c("RUNX1T1", "ASCL1","GAD1","GRIN2A","GRIA2","VAMP2"))
mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
#DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
# cluster 4 also show mature neuron markers
DotPlot(neur.sub, features = mat_neuron)+RotatedAxis()Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: DLG45
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
#DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(neur.sub, features = ex)+RotatedAxis()Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: GRIN3
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DotPlot(neur.sub, features = inh) + RotatedAxis()Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: GAT1, GABR2, GABR1, GBRR1
I’ll separate out Neurons 2,4,6
Idents(neurons.sub) <- 'Level3'
neurons1.sub <- subset(neurons.sub, idents = c("Neurons2","Neurons4","Neurons6"))
ClusterMarkers.n1 <- FindAllMarkers(neurons1.sub, only.pos = TRUE)Calculating cluster Neurons2
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster Neurons4
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s top3 <- ClusterMarkers.n1 %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(neurons1.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons1.sub, features = marker.top3) + RotatedAxis()Warning: Scaling data with a low number of groups may produce misleading results
DotPlot(DA.sub, features = marker.top3) + RotatedAxis()Error in UseMethod(generic = "DefaultAssay", object = object) :
no applicable method for 'DefaultAssay' applied to an object of class "character"
Idents(neur.sub) <- 'Level3'
neurons2.sub <- subset(neur.sub, idents = c("Neurons2","Neurons4","Neurons6"), invert = TRUE)
ClusterMarkers.n2 <- FindAllMarkers(neurons2.sub, only.pos = TRUE)Calculating cluster Neurons1
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster Neurons3
| | 0 % ~calculating
|+ | 1 % ~01s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster Neurons5
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster Neurons8-inh-max
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s top3 <- ClusterMarkers.n2 %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(neurons2.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(neurons2.sub, features = marker.top3) + RotatedAxis()Warning: Scaling data with a low number of groups may produce misleading results
write.csv(ClusterMarkers.n2, paste(output_path,"ClusterMarkersNeurons1358.csv", sep = ""))
Look at the GO terms when separating out the neuron subtypes
group = "Neurons3"
#for(group in group.list){
print(group)
[1] "Neurons3"Up.list <- ClusterMarkers.n2 %>% filter(cluster == group & avg_log2FC > 0)
genes <- Up.list$gene
Er <- enrichr(genes, databases = db)Uploading data to Enrichr... Done.
Querying GO_Cellular_Component_2015... Done.
Querying GO_Biological_Process_2015... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)
t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)
t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)
t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)#}
Compare the neurons1 groups to the neurons2 groups
Check the GO terms and celltype libraries for the two groups of neurons
down.list <- ClusterMarkers %>% filter(avg_log2FC < 0)
genes <- rownames(down.list)
Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
Querying GO_Cellular_Component_2015... Done.
Querying GO_Biological_Process_2015... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)Compare directly Neurons 1 (low CD24) with Neurons 2 (high CD24) For DEG and cell type predictions
seu <- neurons.1.2
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- ScaleData(seu)Centering and scaling data matrix
|
| | 0%
|
|============================================== | 50%
|
|============================================================================================| 100%seu.q <- RunPCA(seu)PC_ 1
Positive: DLK1, CLU, SPARCL1, VIM, PTGDS, GNG11, FTL, TFPI2, APOE, WIF1
S100A10, IFITM2, HPD, IGFBP5, GDF10, CP, ID3, SLCO1C1, CYP1B1, MGST1
PAPPA2, NUPR1, ANXA1, SCRG1, PON2, S100A6, PTN, MT-ND2, RBP4, TTYH1
Negative: CELF4, CHGB, SOX4, ANK3, CACNA2D1, PCLO, INA, PCDH9, SYT1, SSTR2
STMN2, STMN1, OCIAD2, TENM3, KLC1, GRIA2, AMER2, DYNC1I1, STMN4, DCX
ZFHX4, NRXN1, NEUROD1, ATCAY, NCKAP5, TERF2IP, PTPRR, MARCH1, EOMES, NREP
PC_ 2
Positive: ASCL1, TMSB4X, CD24, TPBG, RUNX1T1, PAX6, CMIP, CCNG2, FOS, EGR1
DDC, CDH7, PTMA, GPHN, ZIC2, KCNQ1OT1, EFNB2, TEAD1, CADM2, ZIC1
CHD7, DSP, CDKN1C, JUNB, HES6, ZNF385D, STXBP5L, RNASEH2B, SOX4, PLCG2
Negative: MT-CO1, MT-ATP6, MT-CO2, MT-CYB, MT-CO3, MT-ND2, MT-ND3, MT-ND4, MT-ND1, MT-ND5
MALAT1, MT-ND4L, MT-ND6, PLPPR4, NEUROD1, IMPG2, PCAT4, PTPRR, SST, FAM19A4
AANAT, BCAT1, GSG1, TPH1, SGIP1, OLFM3, TTR, RBFOX1, BTBD8, TRIM9
PC_ 3
Positive: PCAT4, TPH1, IMPG2, NCKAP5, FAM19A4, PLPPR4, BTBD8, GSG1, BCAT1, GNB3
NEUROD1, SST, AANAT, GK5, RBFOX1, CLSTN2, LHX4, ANKRD33B, CRABP2, ETV3L
TRIM9, AIPL1, CHRM3, AC004852.2, PRKG2, TMSB4X, NTM, OLFM3, SLC1A2, AP000459.1
Negative: MT-ATP6, MT-CO2, MT-CO1, MT-ND3, MT-CO3, MT-CYB, MT-ND4, MT-ND2, MT-ND1, MT-ND5
EOMES, RASGRP1, MGAT4C, RAB3B, ELAVL3, ELAVL4, MT-ND4L, TSHZ2, SCN9A, ATP8A2
LHX1, PTPRO, SLIT1, MALAT1, ADCYAP1, RGS4, KRT222, SLC16A12, BDNF, GRIA1
PC_ 4
Positive: TFPI2, LY6H, PEG10, SCG2, RASGRP1, S100A10, EOMES, TSHZ2, DNER, VIM
ELAVL4, RFX4, PHLDA1, ANXA1, DYNC1I1, MGAT4C, SLC16A12, PPP1R14C, ATP8A2, PTPRO
PTPRZ1, ID4, MGST1, GNG11, CLU, CHGB, ACSL4, NTRK2, KRT222, STMN2
Negative: MT-CYB, MT-ATP6, MT-ND2, MT-CO1, MT-CO3, MT-CO2, MT-ND3, MT-ND4, MT-ND1, MT-ND5
DDC, AFF2, CDHR1, PROM1, MT-ND4L, MALAT1, PPP1R1B, THSD7A, RUNX1T1, CBLN2
RNASEH2B, MT-ND6, PTGDS, PCDH17, PROX1, CDKN1C, ADRA2C, CDH7, ASCL1, DSP
PC_ 5
Positive: SLC17A6, SOX4, CHGB, TMSB4X, GK5, EOMES, CELF4, PTMA, HES6, KHDRBS2
TOX3, STMN1, NEUROD2, PRDM8, BHLHE22, NEUROD1, NREP, PTPRO, CACNA2D1, MGP
FABP7, CADPS, EGR1, DYNC1I1, TPBG, ADCYAP1, CYP1B1, INSM1, SEMA3A, NPS
Negative: PTN, TFPI2, S100A10, DLK1, LY6H, PON2, VIM, GNG11, MGST1, CLU
TTYH1, SCG2, IFI27, ITM2C, ANXA1, RFX4, SPARCL1, SERPINI1, FTL, FGFBP1
ITPR2, GALNT1, APOE, NTRK2, PEG10, COL1A1, SCRG1, GRIN2A, PGA5, SLC2A1 DimPlot(seu.q)
Idents(seu.q) <- 'Level3'
cluster.ids <- c("Neurons-CD24","Neurons","Neurons-CD24","Neurons",
"Neurons-CD24","DAN",
"Neurons","Neurons-CD24","DAN","DAN"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$NeuronGroups <- Idents(seu.q)
DimPlot(seu.q, group.by = 'Level3', split.by = 'orig.ident')
DimPlot(seu.q, group.by = 'NeuronGroups', split.by = 'orig.ident')
See the predicted markers
Double check and rename the two neuron populations
FeaturePlot(neurons.sub, features = c("CD24","NCAM1", "MAP2", "TUBB3"), split.by = "orig.ident")Warning in FeaturePlot(neurons.sub, features = c("CD24", "NCAM1", "MAP2", :
All cells have the same value (0) of TUBB3.
Look at cell marker libraries and GO terms in all neurons contrasts
library(enrichR)
setEnrichrSite("Enrichr") # Human genes
# list of all the databases
# libaries with cell types
dbs <- listEnrichrDbs()
dbs
db <- c('GO_Cellular_Component_2015','GO_Biological_Process_2015',
'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')
# enrichr(genes, databases = NULL)
group.list <- as.character(unique(ClusterMarkers$cluster))
# try to run a loop
# the loop runs but it's hard to tell the output
# need to run one at a time
[1] "Neurons1" "Neurons2" "Neurons3" "Neurons4" "Neurons5"
[6] "DANeurons1" "Neurons6" "Mix" "Neurons8-inh-max" "DANeurons2"
[11] "DANeurons3"
group.list = "DANeurons3"
for(group in group.list){
print(group)
Up.list <- ClusterMarkers %>% filter(cluster == group & avg_log2FC > 0)
genes <- Up.list$gene
Er <- enrichr(genes, databases = db)
print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)
t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)
t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)
t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)
}
Label transfer Kamath DA neurons in Neurons Subset (has both DA neurons and Others as predicted by AIW002 label transfers)
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")
Idents(seu.r) <- "Cell_Subtype"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Subtype, k.weight = 30)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "da.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$da.pred)
neurons.sub$da.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.55, neurons.sub$da.pred, "none")
DimPlot(neurons.sub, group.by = 'da.pred.thresh')
table(neurons.sub$da.pred.thresh)
See the predictions from Kamath DA subtypes
#DA subtypes
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$da.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$da.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Label from Bhadari adult brain -all brain down sampled
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Karolinski_DevForebrain_downsample_Level1.RDS")
Idents(seu.r) <- "Level1"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Level1, k.weight = 50)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "Bha.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$Bha.pred)
neurons.sub$Bha.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.80, neurons.sub$Bha.pred, "none")
DimPlot(neurons.sub, group.by = 'Bha.pred.thresh')
table(neurons.sub$Bha.pred.thresh)
#Whole adult brain
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$Bha.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$Bha.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)Try with only the midbrain and striatum Bhaduri
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_midbrain_striatum.RDS")
Idents(seu.r) <- "cell_cluster"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cell_cluster, k.weight = 50)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "Bha.m.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$Bha.m.pred)
neurons.sub$Bha.m.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.70, neurons.sub$Bha.m.pred, "none")
DimPlot(neurons.sub, group.by = 'Bha.m.pred.thresh')
table(neurons.sub$Bha.m.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$Bha.m.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$Bha.m.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Use the developing forbrain dataset Karolinski
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Karolinski_DevForebrain_downsample_Level1.RDS")
Idents(seu.r) <- "Clusters"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Clusters, k.weight = 50)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "K.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$K.pred)
neurons.sub$K.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.70, neurons.sub$K.pred, "none")
DimPlot(neurons.sub, group.by = 'K.pred.thresh')
table(neurons.sub$K.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$K.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$K.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Nowakowski developing cortex
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Nowakowski_dev_cortext.RDS")
Idents(seu.r) <- "WGCNAcluster"
Idents(neurons.sub) <- "Level3"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = neurons.sub, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$WGCNAcluster, k.weight = 50)
neurons.sub <- AddMetaData(neurons.sub, predictions$predicted.id, col.name = "N.pred")
neurons.sub <- AddMetaData(neurons.sub, predictions$prediction.score.max, col.name = "prediction.score.max")
table(neurons.sub$N.pred)
neurons.sub$N.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.70, neurons.sub$N.pred, "none")
DimPlot(neurons.sub, group.by = 'N.pred.thresh')
table(neurons.sub$N.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$N.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$N.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Check the DA neurons and None-neurons in the Kamath
neurons.sub$neur.pred.thresh <- ifelse(neurons.sub$prediction.score.max > 0.45, neurons.sub$neur.pred, "none")
DimPlot(neurons.sub, group.by = 'neur.pred.thresh')
table(neurons.sub$neur.pred.thresh)
Ex_LAMP5_BAIAP3 none SOX6_DDT
357 2374 615 # all predictions
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$neur.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(neurons.sub$Level3, neurons.sub$neur.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)Label again after more transfer labels
Subset out the Astrocytes for annotation
Idents(seu.q) <- 'Level3'
# Level 3
cluster.ids <- c("Astro1","Neurons1","RG1","Astro2","Neurons2",
"Astro3","Astro4","Neurons3","RG2","Neurons4",
"Astro5","Neurons5","RG3-NPC","DANeurons1","Neurons6",
"Mix","RG4","Endothelial-NPC","Neurons8-inh-max","DANeurons2",
"DANeurons3","RG5","RG6-epi","RG7","RG-OPC"
)
astro.list <- c("Astro1","Astro2","Astro3","Astro4",
"Astro5","Mix"
)
astro.sub <- subset(seu.q, idents = astro.list)
table(astro.sub$Level3)
DimPlot(astro.sub, label = TRUE, repel = TRUE)
Identify markers of astrocyte subgroups
Idents(seu.q) <- 'Level3'
ClusterMarkers <- FindAllMarkers(astro.sub)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(astro.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(astro.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersLevel3AstroSub.csv"))
Astro 1 and 2, Astro 3 and 4 are similar in makers. These might be merged.
Find the markers between astro 3 and 4 only
Idents(seu.q) <- 'Level3'
ClusterMarkers34 <- FindMarkers(astro.sub, ident.1 = "Astro3", ident.2 = "Astro4", only.pos = TRUE)
ClusterMarkers34 <- FindMarkers(astro.sub, ident.1 = "Astro4", ident.2 = "Astro3", only.pos = TRUE)
top.up <- ClusterMarkers34 %>% top_n(n=5, wt = avg_log2FC)
top.down <- ClusterMarkers34 %>% top_n(n=-5, wt = avg_log2FC)
DoHeatmap(astro.sub, features = c(rownames(top.up),rownames(top.down)), size=3, angle =90, group.bar.height = 0.02)
# these are all up in astro4
up.down <- c("SPINT2", "RGCC", "NNMT", "TRA2B", "BDP1",
"DDR2", "COPB2", "SELENOS", "ALDH3A2", "DDX42", "CP", "CPVL", "ARL4C")
# the expression was all low, higher in mixed
# no good markers
# up in astro3 but not significant
up.down <- c("ATP1A2", "PTN", "APOD", "DLK1", "PABPN1", "TTYH1", "CDO1")
DotPlot(astro.sub, features = up.down) + RotatedAxis()
# PTN is up in Astro3
I will merge Astro3 and Astro4 Maybe 1 and 2 also need to be merged
Idents(astro.sub) <- 'Level3'
ClusterMarkers12 <- FindMarkers(astro.sub, ident.1 = "Astro1", ident.2 = "Astro2", only.pos = TRUE)
ClusterMarkers21 <- FindMarkers(astro.sub, ident.1 = "Astro2", ident.2 = "Astro1", only.pos = TRUE)
# astro2 up
up.down <- c("ESM1", "RSPO2", "HTRA1", "KCNQ10T1", "SOX2", "CCSER2", "CALD1", "PJA2")
DotPlot(astro.sub, features = up.down) + RotatedAxis()
# good markers astro2: ESM1, SOX2, PJA2
# astro1 up not significant
up.down <- c("NDUFAB1", "RPS29", "ATP5PO", "RNF5", "HDDC2", "POLR1D", "FAM229B", "EEF1D")
DotPlot(astro.sub, features = up.down) + RotatedAxis()
# not good marker for astro 1
I’ll merge astro1 and astro2, also astro3 and astro4
Idents(seu.q) <- 'RNA_snn_res.1.8'
#Idents(seu.q) <- 'Level3'
# Level 3
# merge astro1 + astro2 = astro1, astro3+ astro4 = astro2, now astro5 = astro3
cluster.ids <- c("Astro1","Neurons1","RG1","Astro1","Neurons2",
"Astro2","Astro2","Neurons3","RG2","Neurons4",
"Astro3","Neurons5","RG3-NPC","DANeurons1","Neurons6",
"Mix","RG4","Endothelial-NPC","Neurons8-inh-max","DANeurons2",
"DANeurons3","RG5","RG6-epi","RG7","RG-OPC"
)
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Level4 <- Idents(seu.q)
astro.list <- c("Astro1","Astro2","Astro3","Mix"
)
astro.sub <- subset(seu.q, idents = astro.list)
table(astro.sub$Level4)
DimPlot(astro.sub, group.by = "Level4")
# cluster markers with new Astro groupings
Idents(seu.q) <- 'Level4'
ClusterMarkers <- FindAllMarkers(astro.sub, only.pos = TRUE)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(astro.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(astro.sub, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersLevel4AstroSub.csv"))
### how will this look without mix?
astro.list <- c("Astro1","Astro2","Astro3"
)
astro.sub2 <- subset(seu.q, idents = astro.list)
table(astro.sub2$Level4)
DimPlot(astro.sub2, group.by = "Level4")
Idents(seu.q) <- 'Level4'
ClusterMarkers <- FindAllMarkers(astro.sub2, only.pos = TRUE)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(astro.sub2, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
marker.top3 <- unique(top3$gene)
DotPlot(astro.sub2, features = marker.top3) + RotatedAxis()
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersLevel4AstroSubnoMix.csv"))
There are clearer markers without the mix population
Check the GO terms in the astrocyte subtypes
library(enrichR)
setEnrichrSite("Enrichr") # Human genes
# list of all the databases
# libaries with cell types
dbs <- listEnrichrDbs()
dbs
db <- c('GO_Cellular_Component_2015','GO_Biological_Process_2015',
'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')
# enrichr(genes, databases = NULL)
group.list <- as.character(unique(ClusterMarkers$cluster))
group <- "Astro1"
#for(group in group.list){
print(group)
Up.list <- ClusterMarkers %>% filter(cluster == group & avg_log2FC > 0)
genes <- Up.list$gene
Er <- enrichr(genes, databases = db)
print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)
t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)
t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)
t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)
#}
Predict the Astrocyte subgroups
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Nowakowski_dev_cortext.RDS")
Idents(seu.r) <- "WGCNAcluster"
Idents(astro.sub2) <- "Level4"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = astro.sub2, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$WGCNAcluster, k.weight = 50)
astro.sub2 <- AddMetaData(astro.sub2, predictions$predicted.id, col.name = "N.pred")
astro.sub2 <- AddMetaData(astro.sub2, predictions$prediction.score.max, col.name = "prediction.score.max")
table(astro.sub2$N.pred)
astro.sub2$N.pred.thresh <- ifelse(astro.sub2$prediction.score.max > 0.50, astro.sub2$N.pred, "none")
DimPlot(astro.sub2, group.by = 'N.pred.thresh')
table(astro.sub2$N.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$N.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$N.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Predictions developing forebrain
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Karolinski_DevForebrain_downsample_Level1.RDS")
Idents(seu.r) <- "Clusters"
Idents(astro.sub2) <- "Level4"
astro.sub2 <- ScaleData(astro.sub2)
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = astro.sub2, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Clusters, k.weight = 30)
astro.sub2 <- AddMetaData(astro.sub2, predictions$predicted.id, col.name = "k.pred")
astro.sub2 <- AddMetaData(astro.sub2, predictions$prediction.score.max, col.name = "prediction.score.max")
table(astro.sub2$k.pred)
astro.sub2$k.pred.thresh <- ifelse(astro.sub2$prediction.score.max > 0.70, astro.sub2$k.pred, "none")
DimPlot(astro.sub2, group.by = 'N.pred.thresh')
table(astro.sub2$k.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$k.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$k.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Kamath astrocyte subgroups
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/PD_astro.Rds")
Idents(seu.r) <- "Cell_Subtype"
Idents(astro.sub2) <- "Level4"
seu.r <- NormalizeData(seu.r, normalization.method = "LogNormalize", scale.factor = 10000)
seu.r <- FindVariableFeatures(seu.r, selection.method = "vst", nfeatures = 2000)
seu.r <- ScaleData(seu.r)
seu.r <- RunPCA(seu.r)
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = astro.sub2, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$Cell_Subtype, k.weight = 30)
astro.sub2 <- AddMetaData(astro.sub2, predictions$predicted.id, col.name = "Mas.pred")
astro.sub2 <- AddMetaData(astro.sub2, predictions$prediction.score.max, col.name = "prediction.score.max")
table(astro.sub2$Mas.pred)
astro.sub2$Mas.pred.thresh <- ifelse(astro.sub2$prediction.score.max > 0.90, astro.sub2$Mas.pred, "none")
DimPlot(astro.sub2, group.by = 'Mas.pred.thresh')
table(astro.sub2$Mas.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$Mas.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$Mas.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Adult brain midbrain and striatum
seu.r <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PublicData/Bhaduri_midbrain_striatum.RDS")
Idents(seu.r) <- "cell_cluster"
Idents(astro.sub2) <- "Level4"
# find the reference anchors
anchors <- FindTransferAnchors(reference = seu.r, query = astro.sub2, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = seu.r$cell_cluster, k.weight = 30)
# note that if there are not enough anchors you need to reduce the k.weight from default 50
astro.sub2 <- AddMetaData(astro.sub2, predictions$predicted.id, col.name = "Bha.pred")
astro.sub2 <- AddMetaData(astro.sub2, predictions$prediction.score.max, col.name = "prediction.score.max")
table(astro.sub2$Bha.pred)
astro.sub2$Bha.pred.thresh <- ifelse(astro.sub2$prediction.score.max > 0.90, astro.sub2$Bha.pred, "none")
DimPlot(astro.sub2, group.by = 'Bha.pred.thresh')
table(astro.sub2$Bha.pred.thresh)
# all predicitions
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$Bha.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
# with threshold
t.lables <- as.data.frame(table(astro.sub2$Level4, astro.sub2$Bha.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top.thresh <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top.thresh) <- NULL
df.top.thresh$I <- row.names(df.top.thresh)
Will be best to choose some subtype markers Astro1 - most predicted as astrocytes, has GO terms differentation Astro2 - GO terms involving extracellular matrix Astro3 - Go terms involving cell adhsion and proteins
Subset Radial Glia to get subtypes
First calculate DGE and determine if some subgroups should be merged
DoHeatmap(RG.sub, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(RG.sub, features = top3$gene, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: RPL21, RPL34
marker.top3 <- unique(top3$gene)
DotPlot(RG.sub, features = marker.top3) + RotatedAxis()
output_path <- "/Users/rhalenathomas/Documents/Projects_Papers/PhenoID/ForFigures/scRNA/"
write.csv(ClusterMarkers, paste(output_path,"ClusterMarkersRG.csv"))Many significant markers but not a large LFC for any Indeed the marker genes are highly overlapping between many groups RG7 and RG-OPC are distinctive from the others
RG1 markers are expressed in all groups RG1,RG2,RG3,RG4 are all very overlapping RG5 RG6 are overlapping
RG5 and RG6 aren’t even in the Radial Glia sorted population
Subset just the original RG population and look for markers there
Idents(RG.sub) <- 'orig.ident'
RG.FACS <- subset(RG.sub, idents = "RadialGlia")
DimPlot(RG.FACS, group.by = 'Level3')
NA
NAGet markers for these groups
Maybe RG1 and RG2 should be merged
clustermarkers.2.4 <- FindMarkers(RG.FACS, ident.1 = "RG2", ident.2 = "RG4")
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s No positive markers for RG1 vs RG2 however there are lots of up regulated markers in RG2
Subset out RG1-4
Idents(RG.sub) <- 'Level3'
RG.sub2 <- subset(RG.sub, idents = c("RG1","RG2","RG3-NPC","RG4"))
Idents(RG.sub2) <- 'Level3'
ClusterMarkers <- FindAllMarkers(RG.sub2, only.pos = TRUE)Calculating cluster RG1
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster RG2
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster RG3-NPC
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s Calculating cluster RG4
| | 0 % ~calculating
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|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s View(clustermarkers.1.2)
top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(RG.sub2, features = top3$gene, size=3, angle =90, group.bar.height = 0.02)Warning in DoHeatmap(RG.sub2, features = top3$gene, size = 3, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: RPL34, RPS12
marker.top3 <- unique(top3$gene)
DotPlot(RG.sub2, features = marker.top3) + RotatedAxis()Warning: Scaling data with a low number of groups may produce misleading results
top.10 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=10, wt = avg_log2FC)
RG2 and 4 are also overlapping in marker expression - but in 2 vs 4 there are positive markers in both directions
I will merge RG2 and RG4
Test if RG5 and RG6 should be merged into one group
clustermarkers.5.6 <- FindMarkers(RG.sub, ident.1 = "RG5", ident.2 = "RG6-epi")
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~06s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s There are very different and shouldn’t be merged
Check the cluster markers again
Idents(RG.sub) <- 'Level4'
ClusterMarkers <- FindAllMarkers(RG.sub, only.pos = TRUE)Calculating cluster RG1
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster RG2
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~06s
|++ | 3 % ~06s
|+++ | 4 % ~06s
|+++ | 5 % ~06s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 9 % ~05s
|+++++ | 10% ~05s
|++++++ | 11% ~05s
|++++++ | 12% ~05s
|+++++++ | 13% ~05s
|+++++++ | 14% ~05s
|++++++++ | 15% ~05s
|++++++++ | 16% ~05s
|+++++++++ | 17% ~05s
|++++++++++ | 18% ~05s
|++++++++++ | 19% ~05s
|+++++++++++ | 20% ~05s
|+++++++++++ | 21% ~05s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s Calculating cluster RG3
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
|+++++ | 9 % ~04s
|++++++ | 10% ~04s
|++++++ | 11% ~04s
|+++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster RG4
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~03s
|++ | 3 % ~02s
|+++ | 5 % ~02s
|+++ | 6 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster epi
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster RG5
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster opc
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~04s
|++++++ | 10% ~04s
|++++++ | 11% ~04s
|+++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 14% ~04s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|+++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s top3 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=3, wt = avg_log2FC)
DoHeatmap(RG.sub, features = top3$gene, size=5, angle =90, group.bar.height = 0.02)Warning in DoHeatmap(RG.sub, features = top3$gene, size = 5, angle = 90, :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: RPS13, RPL21, RPL34
marker.top3 <- unique(top3$gene)
DotPlot(RG.sub, features = marker.top3) + RotatedAxis()
top.10 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=10, wt = avg_log2FC)The markers are all actually lower even though it says a positive LFC for RG1 (merged with 2)
Idents(RG.sub) <- "Level3"
RG1.markers.1 <- FindMarkers(RG.sub, ident.1 = "RG1", ident.2 = c("RG2","RG-OPC"))
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 6 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~04s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
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|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
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|+++++++++++++++++++++++++ | 50% ~01s
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|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
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|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s RG1.markers.2 <- FindMarkers(RG.sub, ident.1 = "RG1", ident.2 = c("RG2","RG3-NPC","RG4" ))
| | 0 % ~calculating
|+ | 1 % ~07s
|++ | 2 % ~07s
|++ | 3 % ~07s
|+++ | 4 % ~07s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~06s
|+++++ | 9 % ~06s
|++++++ | 10% ~06s
|++++++ | 11% ~06s
|+++++++ | 12% ~06s
|+++++++ | 13% ~06s
|++++++++ | 14% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~05s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 25% ~05s
|++++++++++++++ | 26% ~05s
|++++++++++++++ | 27% ~05s
|+++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s RG1.markers.3 <- FindMarkers(RG.sub, ident.1 = "RG1", ident.2 = c("RG2","RG3-NPC","RG4","RG5","RG6-epi","RG-OPC"))
| | 0 % ~calculating
|+ | 1 % ~07s
|++ | 2 % ~07s
|++ | 3 % ~06s
|+++ | 4 % ~06s
|+++ | 5 % ~06s
|++++ | 6 % ~06s
|++++ | 7 % ~06s
|+++++ | 9 % ~06s
|+++++ | 10% ~06s
|++++++ | 11% ~06s
|++++++ | 12% ~06s
|+++++++ | 13% ~06s
|+++++++ | 14% ~06s
|++++++++ | 15% ~06s
|++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~05s
|+++++++++++ | 20% ~05s
|+++++++++++ | 21% ~05s
|++++++++++++ | 22% ~05s
|++++++++++++ | 23% ~05s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 26% ~05s
|++++++++++++++ | 27% ~05s
|++++++++++++++ | 28% ~05s
|+++++++++++++++ | 29% ~05s
|+++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|++++++++++++++++++++++ | 43% ~04s
|++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|+++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s table(RG.sub$Level3)
Astro1 Neurons1 RG1 Astro2 Neurons2 Astro3
0 0 817 0 0 0
Astro4 Neurons3 RG2 Neurons4 Astro5 Neurons5
0 0 341 0 0 0
RG3-NPC DANeurons1 Neurons6 Mix RG4 Endothelial-NPC
304 0 0 0 211 0
Neurons8-inh-max DANeurons2 DANeurons3 RG5 RG6-epi RG7
0 0 0 99 57 45
RG-OPC
39 I’ll have a look at the predictions and marker list
# predictions
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$Bha.mid.stri.pred))
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$AIW60.pred))
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$AIW120.pred))
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$MBOAIW.pred)) # same as AIW120 but predictions are slighlty different, mostly the same
t.lables <- as.data.frame(table(RG.sub$Level3, RG.sub$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype <- as.data.frame(t.lables %>% group_by(Var1) %>% top_n(2, Freq))
df.top <- top.pred.celltype[order(top.pred.celltype$Var1,-top.pred.celltype$Freq),]
row.names(df.top) <- NULL
df.top$I <- row.names(df.top)
There are not really RG in mature brains
Do some more predictions with the developing Brain
Now the developing cortex data
Have a look at the potential markers for RG1 and RG2
markers <- c("TMSB4X", "TFPI2","RBP1", "LRRC75A", "RPS12", "RPL34", "RPS13")
DotPlot(RG.sub, features= markers, group.by = 'Level3') +RotatedAxis()
DotPlot(RG.sub, features= markers, group.by = 'Level4') +RotatedAxis()
All the RG1 markers are high in RG-OPC, but that small cluster is predicted differently from RG1
Clustermarkers <- FindAllMarkers(RG.sub, test.use = "MAST", only.pos = TRUE)
Calculating cluster RG1
Completed [==>-------------------------------------------------------------------] 5% with 0 failures
Completed [===>------------------------------------------------------------------] 5% with 0 failures
Completed [===>------------------------------------------------------------------] 6% with 0 failures
Completed [====>-----------------------------------------------------------------] 7% with 0 failures
Completed [====>-----------------------------------------------------------------] 8% with 0 failures
Completed [=====>----------------------------------------------------------------] 8% with 0 failures
Completed [=====>----------------------------------------------------------------] 9% with 0 failures
Completed [======>---------------------------------------------------------------] 9% with 0 failures
Completed [======>---------------------------------------------------------------] 10% with 0 failures
Completed [======>---------------------------------------------------------------] 11% with 0 failures
Completed [=======>--------------------------------------------------------------] 11% with 0 failures
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG2
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG3-NPC
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG4
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG5
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG6-epi
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG7
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
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Done!
Calculating cluster RG-OPC
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Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
Completed [>---------------------------------------------------------------------] 2% with 0 failures
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Done!Check out the down regulated pathways
Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
Querying GO_Cellular_Component_2015... Done.
Querying GO_Biological_Process_2015... Done.
Querying Human_Gene_Atlas... Done.
Querying KEGG_2013... Done.
Parsing results... Done.print(plotEnrich(Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
print(plotEnrich(Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value"))
t.GOcell <- Er[[1]] %>% select(Term, Genes, Combined.Score)
print(t.GOcell)
t.GObio <- Er[[2]] %>% select(Term, Genes, Combined.Score)
print(t.GObio)
t.CellMarker <- Er[[3]] %>% select(Term, Genes, Combined.Score)
print(t.CellMarker)
t.Azi <- Er[[4]] %>% select(Term, Genes, Combined.Score)
print(t.Azi)
# noCheck markers from Pollen … Kriegstein
# RG markers were up in one group of RG and down another
# SLC1A3, PAX6, SOX2, PDGFD, GLI3 up in own group, the other has usp STMN2 and NEUROD6
# rg.con <- c("VIM","HES")
rg <- c("SLC1A3", "PAX6", "SOX2", "PDGFD", "GLI3", "STMN2", "NEUROD6", "VIM", "HES1")
DotPlot(RG.sub, features= rg, group.by = 'Level3') +RotatedAxis()
# RG4 matches the first pop SLCI1A3, PAX6 SOX2
# progenitor markers canonical and novel
# EOMES = TBR2
pro <- c("TBR2", "ELAVL4", "NEUROG1", "NEUROD1", "NEUROD4", "PPP1R17", "PENK")
DotPlot(RG.sub, features= pro, group.by = 'Level3') +RotatedAxis()Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: TBR2
# RP7 has some progen markers NEUROD4
# RG-OPC expresses PENK progenitor markers
# CRYAB is a novel G1 marker
# ventral RG markers
rgv <- c("CRYAB", "PDGFD", "TAGLN2", "FBXO32", "PALLD")
# out svz
osvz <- c("HOPX", "PTPRZ1", "TNC", "FAM107A", "MOXD1")
DotPlot(RG.sub, features= rgv, group.by = 'Level3') +RotatedAxis()
DotPlot(RG.sub, features= osvz, group.by = 'Level3') +RotatedAxis()
NA
NARG4,5,6 classic RG markers and indicated as outer SVZ RG RG7 has intermediate progenitor markers RG2 - deviding cells use CRYAB as a marker RG1 is likely the ventral RG - mark as SOX2- RG-OPC has his VIM and also Ventral - TAGLN2
Not sure which marker is good for RG3
rg3 <- c("NEAT1", "POLR2J3.q", "ZMAT1", "DDX17", "PNISR", "LUC7L3",
"DST", "MACF1", "ARGUL1", "NKTR", "WSB1", "SFPQ")
DotPlot(RG.sub, features= rg3, group.by = 'Level3') +RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: POLR2J3.q, ARGUL1
Still need markers for 5 and 6 Check the list and also distinguish from each other
- I’ve already found cluster markers 5 vs 6 above
FeaturePlot(seu.q, features = c("CP", "ECEL1", "EMX2", "VIM", "SOX2","HES1","PAX6"))
DimPlot(seu.q, label = TRUE)
# see each of the markers
rg.sub.markers <- c("SOX2","CRYAB","DDX17","RTPRZ1","CLU","CP","MALAT1","NEUROD4","PENK")
DimPlot(seu.q, label = TRUE)
FeaturePlot(seu.q, features = rg.sub.markers)Warning in FetchData.Seurat(object = object, vars = c(dims, "ident", features), :
The following requested variables were not found: RTPRZ1
FeaturePlot(RG.sub, features = "SOX2", split.by = 'Level3')
# RG-OPC is SOX2 negative
# RG epi and RG7 very low and few
# RG1 mostly negative but a few very positive cells
FeaturePlot(RG.sub, features = "HES1", split.by = 'Level3')
# a little all over almost all cells high in RG4
FeaturePlot(RG.sub, features = "CRYAB", split.by = 'Level3')
Time to add on the final subgroup annotations - double check what makes sense It might be easier to split the Neurons and Radial glia into subgroups and then compare between those subgroups. RG it’s harder different groups overlap
It seems like RG1,2,4 are more similar
RG4 and RG6 are also very similar RG4 is the most ‘true’ RG
RG4 and 2 overlap a lot, also RG6 overlaps with 4 but not with 6
All have VIM RG1 is negative low other markers HES1, SOX2, PAX6 RG-OPC is negative in PAX6 RG5 and 6 have high upregulation of mitochondrial energy production transcripts
MALAT1 high in 5,6,7 and none in the other groups RG7 more and RG-OPC have some intermediate progenitor markers
5 and 6 are not having tones of differentially expressed genes from eachother, but those genes are overlapping with the other groups
I might merge 2 and 4 previously tested 2vs4 and nothing is up in 2 compared to 4
# neuroD1 very high expression in RG7 but maybe not in all the cells
# test many markers - I've type in and removed for speed
# "SPARCL1"
# DLK1 distinguishes 5 vs 6 but it expressed in most other than 6
# save thing for PTN and ESM1
# up in 6 : MGP actually low in 6 but consistant, not in 5
# FHIT no expressed in all 6 and low everywhere
FeaturePlot(RG.sub, features = c("ID1"), split.by = 'Level3')
NA
NA
NA
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